Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations768
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

Numeric8
Categorical1

Alerts

Age is highly overall correlated with PregnanciesHigh correlation
BMI is highly overall correlated with SkinThicknessHigh correlation
Glucose is highly overall correlated with InsulinHigh correlation
Insulin is highly overall correlated with GlucoseHigh correlation
Pregnancies is highly overall correlated with AgeHigh correlation
SkinThickness is highly overall correlated with BMIHigh correlation
Pregnancies has 111 (14.5%) zeros Zeros

Reproduction

Analysis started2024-12-06 04:10:22.402620
Analysis finished2024-12-06 04:10:25.291833
Duration2.89 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Pregnancies
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8450521
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-12-06T01:10:25.328789image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3695781
Coefficient of variation (CV)0.87634133
Kurtosis0.15921978
Mean3.8450521
Median Absolute Deviation (MAD)2
Skewness0.90167398
Sum2953
Variance11.354056
MonotonicityNot monotonic
2024-12-06T01:10:25.376064image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 135
17.6%
0 111
14.5%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
Other values (7) 58
7.6%
ValueCountFrequency (%)
0 111
14.5%
1 135
17.6%
2 103
13.4%
3 75
9.8%
4 68
8.9%
5 57
7.4%
6 50
 
6.5%
7 45
 
5.9%
8 38
 
4.9%
9 28
 
3.6%
ValueCountFrequency (%)
17 1
 
0.1%
15 1
 
0.1%
14 2
 
0.3%
13 10
 
1.3%
12 9
 
1.2%
11 11
 
1.4%
10 24
3.1%
9 28
3.6%
8 38
4.9%
7 45
5.9%

Glucose
Real number (ℝ)

High correlation 

Distinct139
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.56337
Minimum44
Maximum199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-12-06T01:10:25.447980image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile80
Q199
median117
Q3141
95-th percentile181
Maximum199
Range155
Interquartile range (IQR)42

Descriptive statistics

Standard deviation30.550074
Coefficient of variation (CV)0.25130987
Kurtosis-0.27900637
Mean121.56337
Median Absolute Deviation (MAD)20
Skewness0.53024001
Sum93360.667
Variance933.307
MonotonicityNot monotonic
2024-12-06T01:10:25.502142image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 17
 
2.2%
100 17
 
2.2%
129 14
 
1.8%
111 14
 
1.8%
125 14
 
1.8%
106 14
 
1.8%
112 13
 
1.7%
105 13
 
1.7%
102 13
 
1.7%
95 13
 
1.7%
Other values (129) 626
81.5%
ValueCountFrequency (%)
44 1
 
0.1%
56 1
 
0.1%
57 2
0.3%
61 1
 
0.1%
62 1
 
0.1%
65 1
 
0.1%
67 1
 
0.1%
68 3
0.4%
71 4
0.5%
72 1
 
0.1%
ValueCountFrequency (%)
199 1
 
0.1%
198 1
 
0.1%
197 4
0.5%
196 3
0.4%
195 2
0.3%
194 3
0.4%
193 2
0.3%
191 1
 
0.1%
190 1
 
0.1%
189 4
0.5%

BloodPressure
Real number (ℝ)

Distinct64
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.342448
Minimum24
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-12-06T01:10:25.557261image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile52
Q164
median72
Q380
95-th percentile90
Maximum122
Range98
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.22139
Coefficient of variation (CV)0.16893802
Kurtosis0.95530702
Mean72.342448
Median Absolute Deviation (MAD)8
Skewness0.14375588
Sum55559
Variance149.36237
MonotonicityNot monotonic
2024-12-06T01:10:25.610579image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 59
 
7.7%
74 52
 
6.8%
68 46
 
6.0%
78 46
 
6.0%
72 45
 
5.9%
64 44
 
5.7%
76 40
 
5.2%
80 40
 
5.2%
60 37
 
4.8%
62 34
 
4.4%
Other values (54) 325
42.3%
ValueCountFrequency (%)
24 1
 
0.1%
30 2
 
0.3%
38 1
 
0.1%
40 1
 
0.1%
44 4
 
0.5%
46 2
 
0.3%
48 5
 
0.7%
50 13
1.7%
52 11
1.4%
53.33333333 1
 
0.1%
ValueCountFrequency (%)
122 1
 
0.1%
114 1
 
0.1%
110 3
0.4%
108 2
0.3%
106 3
0.4%
104 2
0.3%
102 1
 
0.1%
100 3
0.4%
98 3
0.4%
96 4
0.5%

SkinThickness
Real number (ℝ)

High correlation 

Distinct102
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.099392
Minimum7
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-12-06T01:10:25.664037image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q122.333333
median29
Q335
95-th percentile44.766667
Maximum99
Range92
Interquartile range (IQR)12.666667

Descriptive statistics

Standard deviation9.5803042
Coefficient of variation (CV)0.32922695
Kurtosis3.2256988
Mean29.099392
Median Absolute Deviation (MAD)6.1666667
Skewness0.64650918
Sum22348.333
Variance91.782228
MonotonicityNot monotonic
2024-12-06T01:10:25.775708image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 32
 
4.2%
30 31
 
4.0%
27 29
 
3.8%
33 25
 
3.3%
28 24
 
3.1%
23 23
 
3.0%
31 22
 
2.9%
18 22
 
2.9%
35 22
 
2.9%
26 21
 
2.7%
Other values (92) 517
67.3%
ValueCountFrequency (%)
7 2
 
0.3%
8 2
 
0.3%
10 5
 
0.7%
10.33333333 1
 
0.1%
11 6
 
0.8%
12 7
0.9%
13 11
1.4%
13.66666667 2
 
0.3%
14 6
 
0.8%
15 15
2.0%
ValueCountFrequency (%)
99 1
 
0.1%
63 1
 
0.1%
60 1
 
0.1%
56 1
 
0.1%
54 2
0.3%
53.66666667 1
 
0.1%
52 2
0.3%
51 1
 
0.1%
50 3
0.4%
49 3
0.4%

Insulin
Real number (ℝ)

High correlation 

Distinct417
Distinct (%)54.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.41406
Minimum14
Maximum846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-12-06T01:10:25.826122image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile45
Q183
median132
Q3191
95-th percentile326.86667
Maximum846
Range832
Interquartile range (IQR)108

Descriptive statistics

Standard deviation100.84619
Coefficient of variation (CV)0.65734647
Kurtosis6.8916804
Mean153.41406
Median Absolute Deviation (MAD)53
Skewness2.0267753
Sum117822
Variance10169.955
MonotonicityNot monotonic
2024-12-06T01:10:25.875590image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 11
 
1.4%
140 10
 
1.3%
130 9
 
1.2%
94 8
 
1.0%
120 8
 
1.0%
180 7
 
0.9%
100 7
 
0.9%
115 7
 
0.9%
76 7
 
0.9%
210 6
 
0.8%
Other values (407) 688
89.6%
ValueCountFrequency (%)
14 1
0.1%
15 1
0.1%
16 1
0.1%
18 2
0.3%
22 1
0.1%
23 2
0.3%
25 1
0.1%
29 1
0.1%
30.33333333 1
0.1%
32 1
0.1%
ValueCountFrequency (%)
846 1
0.1%
744 1
0.1%
680 1
0.1%
600 1
0.1%
579 1
0.1%
545 1
0.1%
543 1
0.1%
540 1
0.1%
510 1
0.1%
495 2
0.3%

BMI
Real number (ℝ)

High correlation 

Distinct252
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.426259
Minimum18.2
Maximum67.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-12-06T01:10:25.924126image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum18.2
5-th percentile22.235
Q127.5
median32.15
Q336.6
95-th percentile44.395
Maximum67.1
Range48.9
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation6.889295
Coefficient of variation (CV)0.21246037
Kurtosis0.90096945
Mean32.426259
Median Absolute Deviation (MAD)4.55
Skewness0.60523953
Sum24903.367
Variance47.462385
MonotonicityNot monotonic
2024-12-06T01:10:25.973724image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 13
 
1.7%
31.2 12
 
1.6%
31.6 12
 
1.6%
30.1 10
 
1.3%
32.4 10
 
1.3%
33.3 10
 
1.3%
32.8 9
 
1.2%
30.8 9
 
1.2%
32.9 9
 
1.2%
34.2 8
 
1.0%
Other values (242) 666
86.7%
ValueCountFrequency (%)
18.2 3
0.4%
18.4 1
 
0.1%
19.1 1
 
0.1%
19.3 1
 
0.1%
19.4 1
 
0.1%
19.5 2
0.3%
19.6 3
0.4%
19.9 1
 
0.1%
20 1
 
0.1%
20.1 1
 
0.1%
ValueCountFrequency (%)
67.1 1
0.1%
59.4 1
0.1%
57.3 1
0.1%
55 1
0.1%
53.2 1
0.1%
52.9 1
0.1%
52.3 2
0.3%
50 1
0.1%
49.7 1
0.1%
49.6 1
0.1%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-12-06T01:10:26.023452image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.3313286
Coefficient of variation (CV)0.70215138
Kurtosis5.5949535
Mean0.4718763
Median Absolute Deviation (MAD)0.1675
Skewness1.9199111
Sum362.401
Variance0.10977864
MonotonicityNot monotonic
2024-12-06T01:10:26.076378image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.258 6
 
0.8%
0.254 6
 
0.8%
0.268 5
 
0.7%
0.207 5
 
0.7%
0.261 5
 
0.7%
0.259 5
 
0.7%
0.238 5
 
0.7%
0.19 4
 
0.5%
0.263 4
 
0.5%
0.299 4
 
0.5%
Other values (507) 719
93.6%
ValueCountFrequency (%)
0.078 1
0.1%
0.084 1
0.1%
0.085 2
0.3%
0.088 2
0.3%
0.089 1
0.1%
0.092 1
0.1%
0.096 1
0.1%
0.1 1
0.1%
0.101 1
0.1%
0.102 1
0.1%
ValueCountFrequency (%)
2.42 1
0.1%
2.329 1
0.1%
2.288 1
0.1%
2.137 1
0.1%
1.893 1
0.1%
1.781 1
0.1%
1.731 1
0.1%
1.699 1
0.1%
1.698 1
0.1%
1.6 1
0.1%

Age
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.240885
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.1 KiB
2024-12-06T01:10:26.125125image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.760232
Coefficient of variation (CV)0.35378816
Kurtosis0.64315889
Mean33.240885
Median Absolute Deviation (MAD)7
Skewness1.1295967
Sum25529
Variance138.30305
MonotonicityNot monotonic
2024-12-06T01:10:26.177417image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 72
 
9.4%
21 63
 
8.2%
25 48
 
6.2%
24 46
 
6.0%
23 38
 
4.9%
28 35
 
4.6%
26 33
 
4.3%
27 32
 
4.2%
29 29
 
3.8%
31 24
 
3.1%
Other values (42) 348
45.3%
ValueCountFrequency (%)
21 63
8.2%
22 72
9.4%
23 38
4.9%
24 46
6.0%
25 48
6.2%
26 33
4.3%
27 32
4.2%
28 35
4.6%
29 29
3.8%
30 21
 
2.7%
ValueCountFrequency (%)
81 1
 
0.1%
72 1
 
0.1%
70 1
 
0.1%
69 2
0.3%
68 1
 
0.1%
67 3
0.4%
66 4
0.5%
65 3
0.4%
64 1
 
0.1%
63 4
0.5%

Outcome
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
0
500 
1
268 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters768
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Length

2024-12-06T01:10:26.224671image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-06T01:10:26.265016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring characters

ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 500
65.1%
1 268
34.9%

Interactions

2024-12-06T01:10:24.831201image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:22.545318image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:22.916459image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.236531image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.542498image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.849347image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.149581image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.518541image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.875456image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:22.595610image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:22.958504image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.276722image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.583378image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.889170image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.189888image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.560612image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.918142image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:22.683655image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.001465image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.318024image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.623843image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.931572image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.230122image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.602275image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.957116image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:22.723347image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.041181image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.355098image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.662038image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.968243image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.267588image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.640065image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.993399image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:22.761337image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.079360image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.392180image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.698894image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.004265image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.321300image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.678024image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:25.030036image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:22.798139image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.116637image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.428174image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.736554image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.038361image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.355264image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.714430image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:25.065169image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:22.836346image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.155822image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.465554image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.772986image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.073415image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.445257image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.750556image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:25.139867image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:22.876073image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.195603image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.503505image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:23.810939image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.111672image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.482226image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-12-06T01:10:24.790466image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-12-06T01:10:26.294004image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
AgeBMIBloodPressureDiabetesPedigreeFunctionGlucoseInsulinOutcomePregnanciesSkinThickness
Age1.0000.1230.3800.0430.2900.2820.3140.6070.218
BMI0.1231.0000.3040.1370.2340.2950.3120.0030.674
BloodPressure0.3800.3041.0000.0200.2650.1970.1630.1930.250
DiabetesPedigreeFunction0.0430.1370.0201.0000.0950.1090.173-0.0430.075
Glucose0.2900.2340.2650.0951.0000.6860.4830.1350.242
Insulin0.2820.2950.1970.1090.6861.0000.3510.1250.270
Outcome0.3140.3120.1630.1730.4830.3511.0000.2350.269
Pregnancies0.6070.0030.193-0.0430.1350.1250.2351.0000.105
SkinThickness0.2180.6740.2500.0750.2420.2700.2690.1051.000

Missing values

2024-12-06T01:10:25.196170image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-06T01:10:25.255892image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
06148.072.035.000000125.33333333.60.627501
1185.066.029.00000066.66666726.60.351310
28183.064.030.000000195.00000023.30.672321
3189.066.023.00000094.00000028.10.167210
40137.040.035.000000168.00000043.12.288331
55116.074.018.333333109.00000025.60.201300
6378.050.032.00000088.00000031.00.248261
710115.070.037.666667145.00000035.30.134290
82197.070.045.000000543.00000030.50.158531
98125.096.024.333333191.33333331.90.232541
PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
7581106.076.033.000000171.66666737.50.197260
7596190.092.028.666667257.33333335.50.278661
760288.058.026.00000016.00000028.40.766220
7619170.074.031.000000203.33333344.00.403431
762989.062.017.00000042.00000022.50.142330
76310101.076.048.000000180.00000032.90.171630
7642122.070.027.000000166.66666736.80.340270
7655121.072.023.000000112.00000026.20.245300
7661126.060.035.333333120.66666730.10.349471
767193.070.031.00000070.66666730.40.315230